Overview

Dataset statistics

Number of variables25
Number of observations221209
Missing cells1390304
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.2 MiB
Average record size in memory200.0 B

Variable types

Categorical10
Numeric15

Alerts

PPG has a high cardinality: 314 distinct valuesHigh cardinality
Week has a high cardinality: 185 distinct valuesHigh cardinality
Sub_Brand has a high cardinality: 55 distinct valuesHigh cardinality
F_Group has a high cardinality: 65 distinct valuesHigh cardinality
Pack_Size_Num is highly overall correlated with Base_Price_Per_KG and 9 other fieldsHigh correlation
Category_Value is highly overall correlated with Category_Volume and 2 other fieldsHigh correlation
Category_Volume is highly overall correlated with Category_Value and 2 other fieldsHigh correlation
Base_Volume is highly overall correlated with Non_Promo_Volume and 3 other fieldsHigh correlation
Non_Promo_Volume is highly overall correlated with Base_Volume and 2 other fieldsHigh correlation
Promo_Volume is highly overall correlated with Promo_ValueHigh correlation
Base_Value is highly overall correlated with Base_Volume and 2 other fieldsHigh correlation
Non_Promo_Value is highly overall correlated with Base_Volume and 2 other fieldsHigh correlation
Promo_Value is highly overall correlated with Promo_VolumeHigh correlation
Base_Price_Per_KG is highly overall correlated with Pack_Size_Num and 6 other fieldsHigh correlation
Non_Promo_Price_Per_KG is highly overall correlated with Pack_Size_Num and 7 other fieldsHigh correlation
Promo_Price_Per_KG is highly overall correlated with Pack_Size_Num and 7 other fieldsHigh correlation
Distribution_wtd is highly overall correlated with Distribution_numericHigh correlation
Distribution_numeric is highly overall correlated with Distribution_wtdHigh correlation
Retailer_Name is highly overall correlated with Category_Value and 1 other fieldsHigh correlation
Category is highly overall correlated with Pack_Size_Num and 10 other fieldsHigh correlation
Brand is highly overall correlated with Pack_Size_Num and 4 other fieldsHigh correlation
Sub_Brand is highly overall correlated with Pack_Size_Num and 9 other fieldsHigh correlation
Pack_Type is highly overall correlated with Pack_Size_Num and 6 other fieldsHigh correlation
Pack_Size is highly overall correlated with Pack_Size_Num and 9 other fieldsHigh correlation
Modelling_Group is highly overall correlated with Pack_Size_Num and 6 other fieldsHigh correlation
F_Group is highly overall correlated with Pack_Size_Num and 9 other fieldsHigh correlation
Category_Value has 45032 (20.4%) missing valuesMissing
Category_Volume has 45032 (20.4%) missing valuesMissing
Base_Volume has 88602 (40.1%) missing valuesMissing
Non_Promo_Volume has 94079 (42.5%) missing valuesMissing
Promo_Volume has 189575 (85.7%) missing valuesMissing
Base_Value has 88602 (40.1%) missing valuesMissing
Non_Promo_Value has 94079 (42.5%) missing valuesMissing
Promo_Value has 189575 (85.7%) missing valuesMissing
Base_Price_Per_KG has 88602 (40.1%) missing valuesMissing
Non_Promo_Price_Per_KG has 94079 (42.5%) missing valuesMissing
Promo_Price_Per_KG has 189575 (85.7%) missing valuesMissing
Distribution_wtd has 91859 (41.5%) missing valuesMissing
Distribution_numeric has 91613 (41.4%) missing valuesMissing

Reproduction

Analysis started2023-04-23 15:56:57.867731
Analysis finished2023-04-23 15:57:21.381556
Duration23.51 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PPG
Categorical

Distinct314
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
BRAND F SUBBRAND F.1 ADULT BLIK RND_KP_KN_LV_ZVS_SPN_ND_KLK 680_G_8_ST
 
2535
BRAND F SUBBRAND F.1 ADULT BLIK TON_RND_KLK_LV 680_G_8_ST
 
2535
BRAND D SUBBRAND D.2 SENIOR POUCH KIP_RND_TN_ZLM 1200_G_12_ST
 
2535
BRAND D SUBBRAND D.2 SENIOR POUCH FOR_TN_SR_ZLM 1200_G_12_ST
 
2535
BRAND F SUBBRAND F.1 ADULT BLIK KIP_KN_NR_ZLM 680_G_8_ST
 
2535
Other values (309)
208534 

Length

Max length74
Median length64
Mean length52.379352
Min length21

Characters and Unicode

Total characters11586784
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_G
2nd rowBRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_G
3rd rowBRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_G
4th rowBRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_G
5th rowBRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_G

Common Values

ValueCountFrequency (%)
BRAND F SUBBRAND F.1 ADULT BLIK RND_KP_KN_LV_ZVS_SPN_ND_KLK 680_G_8_ST 2535
 
1.1%
BRAND F SUBBRAND F.1 ADULT BLIK TON_RND_KLK_LV 680_G_8_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 SENIOR POUCH KIP_RND_TN_ZLM 1200_G_12_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 SENIOR POUCH FOR_TN_SR_ZLM 1200_G_12_ST 2535
 
1.1%
BRAND F SUBBRAND F.1 ADULT BLIK KIP_KN_NR_ZLM 680_G_8_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 KITTEN POUCH KIP_RND_TN_ZLM 1200_G_12_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 ADULT POUCH TON_ZLM_KB_SCHL 1200_G_12_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 ADULT POUCH RND_WRT_KP_TM_ZLM_CR_FR_SPR 1200_G_12_ST 2535
 
1.1%
BRAND D SUBBRAND D.2 ADULT POUCH RND_KP_TN_ZLM 1200_G_12_ST 2535
 
1.1%
BRAND F SUBBRAND F.1 ADULT BLIK RND_TM_KP_WRT_KLK_SPN_LM_SPR 680_G_8_ST 2535
 
1.1%
Other values (304) 195859
88.5%

Length

2023-04-23T21:27:21.434484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brand 221209
 
12.5%
subbrand 220533
 
12.5%
adult 183979
 
10.4%
pouch 78270
 
4.4%
zak 58822
 
3.3%
d 56393
 
3.2%
blik 56108
 
3.2%
1200_g_12_st 46644
 
2.6%
f 42081
 
2.4%
j 33870
 
1.9%
Other values (269) 769059
43.5%

Most occurring characters

ValueCountFrequency (%)
1546266
 
13.3%
D 858308
 
7.4%
_ 850364
 
7.3%
A 768310
 
6.6%
B 748368
 
6.5%
N 735751
 
6.3%
R 687995
 
5.9%
U 511157
 
4.4%
L 455252
 
3.9%
T 426923
 
3.7%
Other values (27) 3998090
34.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7830667
67.6%
Space Separator 1546266
 
13.3%
Decimal Number 1121431
 
9.7%
Connector Punctuation 850364
 
7.3%
Other Punctuation 238056
 
2.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 858308
11.0%
A 768310
9.8%
B 748368
9.6%
N 735751
 
9.4%
R 687995
 
8.8%
U 511157
 
6.5%
L 455252
 
5.8%
T 426923
 
5.5%
S 406094
 
5.2%
K 380650
 
4.9%
Other values (14) 1851859
23.6%
Decimal Number
ValueCountFrequency (%)
0 387616
34.6%
1 248033
22.1%
2 156833
14.0%
4 107698
 
9.6%
8 64328
 
5.7%
3 61152
 
5.5%
5 48985
 
4.4%
6 34354
 
3.1%
7 8835
 
0.8%
9 3597
 
0.3%
Space Separator
ValueCountFrequency (%)
1546266
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 850364
100.0%
Other Punctuation
ValueCountFrequency (%)
. 238056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7830667
67.6%
Common 3756117
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 858308
11.0%
A 768310
9.8%
B 748368
9.6%
N 735751
 
9.4%
R 687995
 
8.8%
U 511157
 
6.5%
L 455252
 
5.8%
T 426923
 
5.5%
S 406094
 
5.2%
K 380650
 
4.9%
Other values (14) 1851859
23.6%
Common
ValueCountFrequency (%)
1546266
41.2%
_ 850364
22.6%
0 387616
 
10.3%
1 248033
 
6.6%
. 238056
 
6.3%
2 156833
 
4.2%
4 107698
 
2.9%
8 64328
 
1.7%
3 61152
 
1.6%
5 48985
 
1.3%
Other values (3) 46786
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11586784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1546266
 
13.3%
D 858308
 
7.4%
_ 850364
 
7.3%
A 768310
 
6.6%
B 748368
 
6.5%
N 735751
 
6.3%
R 687995
 
5.9%
U 511157
 
4.4%
L 455252
 
3.9%
T 426923
 
3.7%
Other values (27) 3998090
34.5%

Week
Categorical

Distinct185
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
wk 20 09
 
1297
wk 20 25
 
1297
wk 20 18
 
1297
wk 20 19
 
1297
wk 20 20
 
1297
Other values (180)
214724 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1769672
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwk 18 21
2nd rowwk 18 21
3rd rowwk 18 21
4th rowwk 18 22
5th rowwk 18 22

Common Values

ValueCountFrequency (%)
wk 20 09 1297
 
0.6%
wk 20 25 1297
 
0.6%
wk 20 18 1297
 
0.6%
wk 20 19 1297
 
0.6%
wk 20 20 1297
 
0.6%
wk 20 21 1297
 
0.6%
wk 20 22 1297
 
0.6%
wk 20 23 1297
 
0.6%
wk 20 24 1297
 
0.6%
wk 20 26 1297
 
0.6%
Other values (175) 208239
94.1%

Length

2023-04-23T21:27:21.487548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wk 221209
33.3%
20 73391
 
11.1%
19 72094
 
10.9%
18 52034
 
7.8%
21 42795
 
6.4%
22 5155
 
0.8%
23 5155
 
0.8%
24 5155
 
0.8%
15 4650
 
0.7%
17 4650
 
0.7%
Other values (44) 177339
26.7%

Most occurring characters

ValueCountFrequency (%)
442418
25.0%
1 222045
12.5%
w 221209
12.5%
k 221209
12.5%
2 180120
10.2%
0 124733
 
7.0%
9 88417
 
5.0%
8 68342
 
3.9%
3 64587
 
3.6%
4 60528
 
3.4%
Other values (3) 76064
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 884836
50.0%
Space Separator 442418
25.0%
Lowercase Letter 442418
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 222045
25.1%
2 180120
20.4%
0 124733
14.1%
9 88417
 
10.0%
8 68342
 
7.7%
3 64587
 
7.3%
4 60528
 
6.8%
5 34038
 
3.8%
6 21068
 
2.4%
7 20958
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
w 221209
50.0%
k 221209
50.0%
Space Separator
ValueCountFrequency (%)
442418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1327254
75.0%
Latin 442418
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
442418
33.3%
1 222045
16.7%
2 180120
13.6%
0 124733
 
9.4%
9 88417
 
6.7%
8 68342
 
5.1%
3 64587
 
4.9%
4 60528
 
4.6%
5 34038
 
2.6%
6 21068
 
1.6%
Latin
ValueCountFrequency (%)
w 221209
50.0%
k 221209
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1769672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
442418
25.0%
1 222045
12.5%
w 221209
12.5%
k 221209
12.5%
2 180120
10.2%
0 124733
 
7.0%
9 88417
 
5.0%
8 68342
 
3.9%
3 64587
 
3.6%
4 60528
 
3.4%
Other values (3) 76064
 
4.3%

Week_Num
Real number (ℝ)

Distinct185
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.2514
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:21.545683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q147
median89
Q3132
95-th percentile166
Maximum185
Range184
Interquartile range (IQR)85

Descriptive statistics

Standard deviation49.427722
Coefficient of variation (CV)0.55380332
Kurtosis-1.1818477
Mean89.2514
Median Absolute Deviation (MAD)43
Skewness0.001122795
Sum19743213
Variance2443.0997
MonotonicityNot monotonic
2023-04-23T21:27:21.607500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 1297
 
0.6%
117 1297
 
0.6%
110 1297
 
0.6%
111 1297
 
0.6%
112 1297
 
0.6%
113 1297
 
0.6%
114 1297
 
0.6%
115 1297
 
0.6%
116 1297
 
0.6%
118 1297
 
0.6%
Other values (175) 208239
94.1%
ValueCountFrequency (%)
1 759
0.3%
2 759
0.3%
3 759
0.3%
4 759
0.3%
5 759
0.3%
6 759
0.3%
7 759
0.3%
8 759
0.3%
9 1264
0.6%
10 1264
0.6%
ValueCountFrequency (%)
185 15
 
< 0.1%
184 15
 
< 0.1%
183 15
 
< 0.1%
182 15
 
< 0.1%
181 125
 
0.1%
180 125
 
0.1%
179 125
 
0.1%
178 125
 
0.1%
177 744
0.3%
176 744
0.3%

Retailer_Name
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Retailer B
80210 
Retailer A
73511 
Retailer C
67488 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2212090
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRetailer A
2nd rowRetailer B
3rd rowRetailer C
4th rowRetailer A
5th rowRetailer B

Common Values

ValueCountFrequency (%)
Retailer B 80210
36.3%
Retailer A 73511
33.2%
Retailer C 67488
30.5%

Length

2023-04-23T21:27:21.666441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T21:27:21.725685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
retailer 221209
50.0%
b 80210
 
18.1%
a 73511
 
16.6%
c 67488
 
15.3%

Most occurring characters

ValueCountFrequency (%)
e 442418
20.0%
R 221209
10.0%
t 221209
10.0%
a 221209
10.0%
i 221209
10.0%
l 221209
10.0%
r 221209
10.0%
221209
10.0%
B 80210
 
3.6%
A 73511
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1548463
70.0%
Uppercase Letter 442418
 
20.0%
Space Separator 221209
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 442418
28.6%
t 221209
14.3%
a 221209
14.3%
i 221209
14.3%
l 221209
14.3%
r 221209
14.3%
Uppercase Letter
ValueCountFrequency (%)
R 221209
50.0%
B 80210
 
18.1%
A 73511
 
16.6%
C 67488
 
15.3%
Space Separator
ValueCountFrequency (%)
221209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1990881
90.0%
Common 221209
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 442418
22.2%
R 221209
11.1%
t 221209
11.1%
a 221209
11.1%
i 221209
11.1%
l 221209
11.1%
r 221209
11.1%
B 80210
 
4.0%
A 73511
 
3.7%
C 67488
 
3.4%
Common
ValueCountFrequency (%)
221209
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2212090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 442418
20.0%
R 221209
10.0%
t 221209
10.0%
a 221209
10.0%
i 221209
10.0%
l 221209
10.0%
r 221209
10.0%
221209
10.0%
B 80210
 
3.6%
A 73511
 
3.3%

Category
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
SINGLE SERVE
104634 
DRY
76015 
MULTI SERVE
29744 
WET
 
6084
SNACKS
 
4732

Length

Max length12
Median length11
Mean length8.3969504
Min length3

Characters and Unicode

Total characters1857481
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRY
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowDRY

Common Values

ValueCountFrequency (%)
SINGLE SERVE 104634
47.3%
DRY 76015
34.4%
MULTI SERVE 29744
 
13.4%
WET 6084
 
2.8%
SNACKS 4732
 
2.1%

Length

2023-04-23T21:27:21.775010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T21:27:21.834327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
serve 134378
37.8%
single 104634
29.4%
dry 76015
21.4%
multi 29744
 
8.4%
wet 6084
 
1.7%
snacks 4732
 
1.3%

Most occurring characters

ValueCountFrequency (%)
E 379474
20.4%
S 248476
13.4%
R 210393
11.3%
L 134378
 
7.2%
134378
 
7.2%
V 134378
 
7.2%
I 134378
 
7.2%
N 109366
 
5.9%
G 104634
 
5.6%
Y 76015
 
4.1%
Other values (8) 191611
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1723103
92.8%
Space Separator 134378
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 379474
22.0%
S 248476
14.4%
R 210393
12.2%
L 134378
 
7.8%
V 134378
 
7.8%
I 134378
 
7.8%
N 109366
 
6.3%
G 104634
 
6.1%
Y 76015
 
4.4%
D 76015
 
4.4%
Other values (7) 115596
 
6.7%
Space Separator
ValueCountFrequency (%)
134378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1723103
92.8%
Common 134378
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 379474
22.0%
S 248476
14.4%
R 210393
12.2%
L 134378
 
7.8%
V 134378
 
7.8%
I 134378
 
7.8%
N 109366
 
6.3%
G 104634
 
6.1%
Y 76015
 
4.4%
D 76015
 
4.4%
Other values (7) 115596
 
6.7%
Common
ValueCountFrequency (%)
134378
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1857481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 379474
20.4%
S 248476
13.4%
R 210393
11.3%
L 134378
 
7.2%
134378
 
7.2%
V 134378
 
7.2%
I 134378
 
7.2%
N 109366
 
5.9%
G 104634
 
5.6%
Y 76015
 
4.1%
Other values (8) 191611
10.3%

Brand
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
BRAND D
56393 
BRAND F
42081 
BRAND G
23076 
BRAND L
15792 
BRAND H
15717 
Other values (10)
68150 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1548463
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRAND A
2nd rowBRAND A
3rd rowBRAND A
4th rowBRAND A
5th rowBRAND A

Common Values

ValueCountFrequency (%)
BRAND D 56393
25.5%
BRAND F 42081
19.0%
BRAND G 23076
10.4%
BRAND L 15792
 
7.1%
BRAND H 15717
 
7.1%
BRAND O 15717
 
7.1%
BRAND J 15008
 
6.8%
BRAND M 11709
 
5.3%
BRAND B 8112
 
3.7%
BRAND I 7098
 
3.2%
Other values (5) 10506
 
4.7%

Length

2023-04-23T21:27:21.886007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brand 221209
50.0%
d 56393
 
12.7%
f 42081
 
9.5%
g 23076
 
5.2%
l 15792
 
3.6%
h 15717
 
3.6%
o 15717
 
3.6%
j 15008
 
3.4%
m 11709
 
2.6%
b 8112
 
1.8%
Other values (6) 17604
 
4.0%

Most occurring characters

ValueCountFrequency (%)
D 277602
17.9%
B 229321
14.8%
A 223744
14.4%
N 222730
14.4%
R 221209
14.3%
221209
14.3%
F 42081
 
2.7%
G 23076
 
1.5%
L 15792
 
1.0%
H 15717
 
1.0%
Other values (7) 55982
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1327254
85.7%
Space Separator 221209
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 277602
20.9%
B 229321
17.3%
A 223744
16.9%
N 222730
16.8%
R 221209
16.7%
F 42081
 
3.2%
G 23076
 
1.7%
L 15792
 
1.2%
H 15717
 
1.2%
O 15717
 
1.2%
Other values (6) 40265
 
3.0%
Space Separator
ValueCountFrequency (%)
221209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1327254
85.7%
Common 221209
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 277602
20.9%
B 229321
17.3%
A 223744
16.9%
N 222730
16.8%
R 221209
16.7%
F 42081
 
3.2%
G 23076
 
1.7%
L 15792
 
1.2%
H 15717
 
1.2%
O 15717
 
1.2%
Other values (6) 40265
 
3.0%
Common
ValueCountFrequency (%)
221209
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 277602
17.9%
B 229321
14.8%
A 223744
14.4%
N 222730
14.4%
R 221209
14.3%
221209
14.3%
F 42081
 
2.7%
G 23076
 
1.5%
L 15792
 
1.0%
H 15717
 
1.0%
Other values (7) 55982
 
3.6%

Sub_Brand
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
SUBBRAND J.1
32498 
SUBBRAND F.1
25350 
SUBBRAND D.2
24336 
SUBBRAND F.4
12168 
SUBBRAND D.3
 
11154
Other values (50)
115703 

Length

Max length14
Median length12
Mean length12.158429
Min length12

Characters and Unicode

Total characters2689554
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUBBRAND A.1
2nd rowSUBBRAND A.1
3rd rowSUBBRAND A.1
4th rowSUBBRAND A.1
5th rowSUBBRAND A.1

Common Values

ValueCountFrequency (%)
SUBBRAND J.1 32498
 
14.7%
SUBBRAND F.1 25350
 
11.5%
SUBBRAND D.2 24336
 
11.0%
SUBBRAND F.4 12168
 
5.5%
SUBBRAND D.3 11154
 
5.0%
SUBBRAND G.1 8352
 
3.8%
SUBBRAND O.3 8112
 
3.7%
SUBBRAND D.4 5649
 
2.6%
SUBBRAND H.2 5070
 
2.3%
SUBBRAND H.1 5070
 
2.3%
Other values (45) 83450
37.7%

Length

2023-04-23T21:27:21.937089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
subbrand 221209
50.0%
j.1 32498
 
7.3%
f.1 25350
 
5.7%
d.2 24336
 
5.5%
f.4 12168
 
2.8%
d.3 11154
 
2.5%
g.1 8352
 
1.9%
o.3 8112
 
1.8%
d.4 5649
 
1.3%
h.2 5070
 
1.1%
Other values (46) 88520
20.0%

Most occurring characters

ValueCountFrequency (%)
B 450530
16.8%
D 283758
10.6%
. 238732
8.9%
A 223744
8.3%
N 222730
8.3%
S 221209
8.2%
R 221209
8.2%
221209
8.2%
U 221209
8.2%
1 101371
 
3.8%
Other values (16) 283853
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1990881
74.0%
Other Punctuation 238732
 
8.9%
Decimal Number 238732
 
8.9%
Space Separator 221209
 
8.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 450530
22.6%
D 283758
14.3%
A 223744
11.2%
N 222730
11.2%
S 221209
11.1%
R 221209
11.1%
U 221209
11.1%
F 42081
 
2.1%
J 33870
 
1.7%
G 24018
 
1.2%
Other values (5) 46523
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 101371
42.5%
2 42607
17.8%
3 35151
 
14.7%
4 31191
 
13.1%
5 10284
 
4.3%
6 9680
 
4.1%
7 5793
 
2.4%
8 1593
 
0.7%
9 1062
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 238732
100.0%
Space Separator
ValueCountFrequency (%)
221209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1990881
74.0%
Common 698673
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 450530
22.6%
D 283758
14.3%
A 223744
11.2%
N 222730
11.2%
S 221209
11.1%
R 221209
11.1%
U 221209
11.1%
F 42081
 
2.1%
J 33870
 
1.7%
G 24018
 
1.2%
Other values (5) 46523
 
2.3%
Common
ValueCountFrequency (%)
. 238732
34.2%
221209
31.7%
1 101371
14.5%
2 42607
 
6.1%
3 35151
 
5.0%
4 31191
 
4.5%
5 10284
 
1.5%
6 9680
 
1.4%
7 5793
 
0.8%
8 1593
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2689554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 450530
16.8%
D 283758
10.6%
. 238732
8.9%
A 223744
8.3%
N 222730
8.3%
S 221209
8.2%
R 221209
8.2%
221209
8.2%
U 221209
8.2%
1 101371
 
3.8%
Other values (16) 283853
10.6%

Pack_Type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
POUCH
78777 
ZAK
58822 
BLIK
56108 
PAK
19390 
ALU
 
5577

Length

Max length5
Median length4
Mean length3.9658829
Min length3

Characters and Unicode

Total characters877289
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZAK
2nd rowZAK
3rd rowZAK
4th rowZAK
5th rowZAK

Common Values

ValueCountFrequency (%)
POUCH 78777
35.6%
ZAK 58822
26.6%
BLIK 56108
25.4%
PAK 19390
 
8.8%
ALU 5577
 
2.5%
BAK 2535
 
1.1%

Length

2023-04-23T21:27:21.993081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T21:27:22.055877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
pouch 78777
35.6%
zak 58822
26.6%
blik 56108
25.4%
pak 19390
 
8.8%
alu 5577
 
2.5%
bak 2535
 
1.1%

Most occurring characters

ValueCountFrequency (%)
K 136855
15.6%
P 98167
11.2%
A 86324
9.8%
U 84354
9.6%
O 78777
9.0%
C 78777
9.0%
H 78777
9.0%
L 61685
7.0%
Z 58822
6.7%
B 58643
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 877289
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 136855
15.6%
P 98167
11.2%
A 86324
9.8%
U 84354
9.6%
O 78777
9.0%
C 78777
9.0%
H 78777
9.0%
L 61685
7.0%
Z 58822
6.7%
B 58643
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 877289
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 136855
15.6%
P 98167
11.2%
A 86324
9.8%
U 84354
9.6%
O 78777
9.0%
C 78777
9.0%
H 78777
9.0%
L 61685
7.0%
Z 58822
6.7%
B 58643
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 877289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 136855
15.6%
P 98167
11.2%
A 86324
9.8%
U 84354
9.6%
O 78777
9.0%
C 78777
9.0%
H 78777
9.0%
L 61685
7.0%
Z 58822
6.7%
B 58643
6.7%

Pack_Size
Categorical

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1200_G_12_ST
47151 
1000_G
24379 
400_G
22477 
340_G_4_ST
17889 
800_G
17612 
Other values (19)
91701 

Length

Max length12
Median length6
Mean length7.6516552
Min length4

Characters and Unicode

Total characters1692615
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1500_G
2nd row1500_G
3rd row1500_G
4th row1500_G
5th row1500_G

Common Values

ValueCountFrequency (%)
1200_G_12_ST 47151
21.3%
1000_G 24379
11.0%
400_G 22477
10.2%
340_G_4_ST 17889
 
8.1%
800_G 17612
 
8.0%
85_G 16731
 
7.6%
680_G_8_ST 10647
 
4.8%
1500_G 10140
 
4.6%
2000_G 9688
 
4.4%
405_G 5070
 
2.3%
Other values (14) 39425
17.8%

Length

2023-04-23T21:27:22.114342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1200_g_12_st 47151
21.3%
1000_g 24379
11.0%
400_g 22477
10.2%
340_g_4_st 17889
 
8.1%
800_g 17612
 
8.0%
85_g 16731
 
7.6%
680_g_8_st 10647
 
4.8%
1500_g 10140
 
4.6%
2000_g 9688
 
4.4%
405_g 5070
 
2.3%
Other values (14) 39425
17.8%

Most occurring characters

ValueCountFrequency (%)
_ 403099
23.8%
0 388630
23.0%
G 221209
13.1%
1 148352
 
8.8%
2 115240
 
6.8%
S 90945
 
5.4%
T 90945
 
5.4%
4 76000
 
4.5%
8 62735
 
3.7%
5 39208
 
2.3%
Other values (4) 56252
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 886417
52.4%
Connector Punctuation 403099
23.8%
Uppercase Letter 403099
23.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 388630
43.8%
1 148352
 
16.7%
2 115240
 
13.0%
4 76000
 
8.6%
8 62735
 
7.1%
5 39208
 
4.4%
3 26001
 
2.9%
6 24674
 
2.8%
7 3042
 
0.3%
9 2535
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G 221209
54.9%
S 90945
22.6%
T 90945
22.6%
Connector Punctuation
ValueCountFrequency (%)
_ 403099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1289516
76.2%
Latin 403099
 
23.8%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 403099
31.3%
0 388630
30.1%
1 148352
 
11.5%
2 115240
 
8.9%
4 76000
 
5.9%
8 62735
 
4.9%
5 39208
 
3.0%
3 26001
 
2.0%
6 24674
 
1.9%
7 3042
 
0.2%
Latin
ValueCountFrequency (%)
G 221209
54.9%
S 90945
22.6%
T 90945
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1692615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 403099
23.8%
0 388630
23.0%
G 221209
13.1%
1 148352
 
8.8%
2 115240
 
6.8%
S 90945
 
5.4%
T 90945
 
5.4%
4 76000
 
4.5%
8 62735
 
3.7%
5 39208
 
2.3%
Other values (4) 56252
 
3.3%

Pack_Size_Num
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean851.31138
Minimum45
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:22.168341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile85
Q1400
median800
Q31200
95-th percentile2000
Maximum3000
Range2955
Interquartile range (IQR)800

Descriptive statistics

Standard deviation583.50161
Coefficient of variation (CV)0.68541502
Kurtosis2.05664
Mean851.31138
Median Absolute Deviation (MAD)400
Skewness1.0619604
Sum1.8831774 × 108
Variance340474.13
MonotonicityNot monotonic
2023-04-23T21:27:22.217377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1200 47151
21.3%
1000 24379
11.0%
400 23491
10.6%
340 17889
 
8.1%
800 17612
 
8.0%
85 16731
 
7.6%
680 10647
 
4.8%
1500 10140
 
4.6%
2000 9688
 
4.4%
405 5070
 
2.3%
Other values (13) 38411
17.4%
ValueCountFrequency (%)
45 338
 
0.2%
60 4394
 
2.0%
80 3042
 
1.4%
85 16731
7.6%
160 4056
 
1.8%
288 2028
 
0.9%
300 3549
 
1.6%
340 17889
8.1%
400 23491
10.6%
405 5070
 
2.3%
ValueCountFrequency (%)
3000 4563
 
2.1%
2000 9688
 
4.4%
1500 10140
 
4.6%
1400 4056
 
1.8%
1200 47151
21.3%
1020 4611
 
2.1%
1000 24379
11.0%
950 2535
 
1.1%
800 17612
 
8.0%
750 3042
 
1.4%

Category_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1014
Distinct (%)0.6%
Missing45032
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean297526.93
Minimum32474.9
Maximum1053967.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:22.280876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum32474.9
5-th percentile46135.8
Q1103588.4
median289281.6
Q3397158.6
95-th percentile643952.3
Maximum1053967.6
Range1021492.7
Interquartile range (IQR)293570.2

Descriptive statistics

Standard deviation199481.94
Coefficient of variation (CV)0.67046685
Kurtosis-0.35332846
Mean297526.93
Median Absolute Deviation (MAD)173884.9
Skewness0.57811987
Sum5.2417401 × 1010
Variance3.9793045 × 1010
MonotonicityNot monotonic
2023-04-23T21:27:22.352518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107673 208
 
0.1%
109460.7 208
 
0.1%
133399.6 208
 
0.1%
110532.1 208
 
0.1%
93238.5 208
 
0.1%
139455.2 208
 
0.1%
95988.3 208
 
0.1%
132658.9 208
 
0.1%
159722 208
 
0.1%
172181.7 208
 
0.1%
Other values (1004) 174097
78.7%
(Missing) 45032
 
20.4%
ValueCountFrequency (%)
32474.9 179
0.1%
34300.5 179
0.1%
36040.2 179
0.1%
36369.4 179
0.1%
36799.9 179
0.1%
36916.6 179
0.1%
37438.2 179
0.1%
37583.7 179
0.1%
37674.6 179
0.1%
38320.7 70
 
< 0.1%
ValueCountFrequency (%)
1053967.6 206
0.1%
1011364.6 206
0.1%
992106.9 206
0.1%
847344.4 206
0.1%
829926 206
0.1%
829807.3 206
0.1%
809121.9 206
0.1%
795827.4 206
0.1%
773312.9 206
0.1%
768076.4 137
0.1%

Category_Volume
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1014
Distinct (%)0.6%
Missing45032
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean73972.473
Minimum10367.7
Maximum367289.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:22.417689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10367.7
5-th percentile15017
Q127358.9
median79848.7
Q395051.1
95-th percentile145159.5
Maximum367289.9
Range356922.2
Interquartile range (IQR)67692.2

Descriptive statistics

Standard deviation46501.972
Coefficient of variation (CV)0.62863888
Kurtosis3.1532192
Mean73972.473
Median Absolute Deviation (MAD)38145.7
Skewness1.0226535
Sum1.3032248 × 1010
Variance2.1624334 × 109
MonotonicityNot monotonic
2023-04-23T21:27:22.478060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24160.8 208
 
0.1%
31677.4 208
 
0.1%
42349.9 208
 
0.1%
24550.9 208
 
0.1%
20878.5 208
 
0.1%
32918.2 208
 
0.1%
25580.5 208
 
0.1%
30372.9 208
 
0.1%
64279.4 208
 
0.1%
60452.6 208
 
0.1%
Other values (1004) 174097
78.7%
(Missing) 45032
 
20.4%
ValueCountFrequency (%)
10367.7 179
0.1%
11024.3 179
0.1%
11358.7 179
0.1%
11412 179
0.1%
11531.6 179
0.1%
11550.2 179
0.1%
11683.5 179
0.1%
11930.6 179
0.1%
11986.2 179
0.1%
12013.8 179
0.1%
ValueCountFrequency (%)
367289.9 206
0.1%
307656.2 206
0.1%
303916.6 206
0.1%
268988.8 206
0.1%
261246.4 137
0.1%
245153.7 206
0.1%
238404.4 137
0.1%
236461.8 206
0.1%
223058.5 137
0.1%
220722.3 206
0.1%

Modelling_Group
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
BRAND D WET POUCH ORIGINAL 4(12X100G)
18252 
BRAND D WET CAT MUSE PREMIUM 12X400G
16562 
BRAND F WET CAN GOLD 6(8X85G)
 
13689
BRAND D WET POUCH EDF 4(12X100G)
 
13689
BRAND D DRY REGULAR 5X1KG
 
12874
Other values (20)
146143 

Length

Max length39
Median length33
Mean length30.889652
Min length15

Characters and Unicode

Total characters6833069
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRAND B DRY NUGGETS REGULAR 3X3KG
2nd rowBRAND B DRY NUGGETS REGULAR 3X3KG
3rd rowBRAND B DRY NUGGETS REGULAR 3X3KG
4th rowBRAND B DRY NUGGETS REGULAR 3X3KG
5th rowBRAND B DRY NUGGETS REGULAR 3X3KG

Common Values

ValueCountFrequency (%)
BRAND D WET POUCH ORIGINAL 4(12X100G) 18252
 
8.3%
BRAND D WET CAT MUSE PREMIUM 12X400G 16562
 
7.5%
BRAND F WET CAN GOLD 6(8X85G) 13689
 
6.2%
BRAND D WET POUCH EDF 4(12X100G) 13689
 
6.2%
BRAND D DRY REGULAR 5X1KG 12874
 
5.8%
BRAND G DRY CAT SPECIALS 4X800G 11293
 
5.1%
BRAND F WET CAN GOLD 4(12X85G) 11202
 
5.1%
BRAND F WET POUCH PERLE 12(4X85G) 10212
 
4.6%
BRAND G DRY CAT REGULAR 4X800G 9748
 
4.4%
BRAND D DRY SPECIALS 5X1KG 9136
 
4.1%
Other values (15) 94552
42.7%

Length

2023-04-23T21:27:22.539141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brand 221209
16.8%
wet 140462
 
10.6%
d 78287
 
5.9%
dry 76015
 
5.8%
pouch 62505
 
4.7%
f 61974
 
4.7%
cat 57390
 
4.3%
regular 55934
 
4.2%
can 55311
 
4.2%
g 40828
 
3.1%
Other values (36) 469463
35.6%

Most occurring characters

ValueCountFrequency (%)
1098169
16.1%
R 466117
 
6.8%
A 455222
 
6.7%
D 423217
 
6.2%
G 396773
 
5.8%
N 337765
 
4.9%
E 320223
 
4.7%
B 253150
 
3.7%
T 229962
 
3.4%
C 225712
 
3.3%
Other values (24) 2626759
38.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4625168
67.7%
Space Separator 1098169
 
16.1%
Decimal Number 904916
 
13.2%
Close Punctuation 93480
 
1.4%
Open Punctuation 93480
 
1.4%
Other Punctuation 17856
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 466117
 
10.1%
A 455222
 
9.8%
D 423217
 
9.2%
G 396773
 
8.6%
N 337765
 
7.3%
E 320223
 
6.9%
B 253150
 
5.5%
T 229962
 
5.0%
C 225712
 
4.9%
X 213030
 
4.6%
Other values (12) 1303997
28.2%
Decimal Number
ValueCountFrequency (%)
0 195027
21.6%
1 177860
19.7%
4 153898
17.0%
5 107996
11.9%
2 107507
11.9%
8 102860
11.4%
3 31714
 
3.5%
6 28054
 
3.1%
Space Separator
ValueCountFrequency (%)
1098169
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93480
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93480
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4625168
67.7%
Common 2207901
32.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 466117
 
10.1%
A 455222
 
9.8%
D 423217
 
9.2%
G 396773
 
8.6%
N 337765
 
7.3%
E 320223
 
6.9%
B 253150
 
5.5%
T 229962
 
5.0%
C 225712
 
4.9%
X 213030
 
4.6%
Other values (12) 1303997
28.2%
Common
ValueCountFrequency (%)
1098169
49.7%
0 195027
 
8.8%
1 177860
 
8.1%
4 153898
 
7.0%
5 107996
 
4.9%
2 107507
 
4.9%
8 102860
 
4.7%
) 93480
 
4.2%
( 93480
 
4.2%
3 31714
 
1.4%
Other values (2) 45910
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6833069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1098169
16.1%
R 466117
 
6.8%
A 455222
 
6.7%
D 423217
 
6.2%
G 396773
 
5.8%
N 337765
 
4.9%
E 320223
 
4.7%
B 253150
 
3.7%
T 229962
 
3.4%
C 225712
 
3.3%
Other values (24) 2626759
38.4%

F_Group
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
BRAND D WET POUCH EDF 4(12X100G)
22815 
BRAND F WET CAN GOLD 24X85G
 
13689
BRAND F WET POUCH PERLE 12(4X85G)
 
12168
BRAND D DRY REGULAR 5X1KG
 
11151
BRAND F WET CAN GOLD 6(8X85G)
 
10140
Other values (60)
151246 

Length

Max length39
Median length31
Mean length26.808426
Min length11

Characters and Unicode

Total characters5930265
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRAND A DRY 1,5KG
2nd rowBRAND A DRY 1,5KG
3rd rowBRAND A DRY 1,5KG
4th rowBRAND A DRY 1,5KG
5th rowBRAND A DRY 1,5KG

Common Values

ValueCountFrequency (%)
BRAND D WET POUCH EDF 4(12X100G) 22815
 
10.3%
BRAND F WET CAN GOLD 24X85G 13689
 
6.2%
BRAND F WET POUCH PERLE 12(4X85G) 12168
 
5.5%
BRAND D DRY REGULAR 5X1KG 11151
 
5.0%
BRAND F WET CAN GOLD 6(8X85G) 10140
 
4.6%
BRAND G DRY CAT SPECIALS 4X800G 9558
 
4.3%
BRAND K DRY REGULAR 2KG 8823
 
4.0%
BRAND D WET POUCH ORIGINAL 4(12X100G) 8112
 
3.7%
BRAND H CAN 400G 8112
 
3.7%
BRAND K CAN 400G 7436
 
3.4%
Other values (55) 109205
49.4%

Length

2023-04-23T21:27:22.598519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brand 221209
18.7%
wet 88869
 
7.5%
dry 76015
 
6.4%
pouch 66561
 
5.6%
d 56393
 
4.8%
can 51545
 
4.4%
regular 44967
 
3.8%
f 42081
 
3.6%
4(12x100g 31941
 
2.7%
specials 28995
 
2.4%
Other values (74) 475364
40.2%

Most occurring characters

ValueCountFrequency (%)
962731
16.2%
R 424764
 
7.2%
D 411415
 
6.9%
A 403254
 
6.8%
G 328291
 
5.5%
N 299842
 
5.1%
E 247636
 
4.2%
B 229321
 
3.9%
0 211660
 
3.6%
C 194682
 
3.3%
Other values (27) 2216669
37.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4011721
67.6%
Space Separator 962731
 
16.2%
Decimal Number 812551
 
13.7%
Open Punctuation 64533
 
1.1%
Close Punctuation 64533
 
1.1%
Other Punctuation 14196
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 424764
 
10.6%
D 411415
 
10.3%
A 403254
 
10.1%
G 328291
 
8.2%
N 299842
 
7.5%
E 247636
 
6.2%
B 229321
 
5.7%
C 194682
 
4.9%
X 158778
 
4.0%
U 148289
 
3.7%
Other values (13) 1165449
29.1%
Decimal Number
ValueCountFrequency (%)
0 211660
26.0%
1 160447
19.7%
4 128701
15.8%
2 98098
12.1%
5 89033
11.0%
8 86249
10.6%
6 20111
 
2.5%
3 12675
 
1.6%
7 3042
 
0.4%
9 2535
 
0.3%
Space Separator
ValueCountFrequency (%)
962731
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64533
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64533
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4011721
67.6%
Common 1918544
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 424764
 
10.6%
D 411415
 
10.3%
A 403254
 
10.1%
G 328291
 
8.2%
N 299842
 
7.5%
E 247636
 
6.2%
B 229321
 
5.7%
C 194682
 
4.9%
X 158778
 
4.0%
U 148289
 
3.7%
Other values (13) 1165449
29.1%
Common
ValueCountFrequency (%)
962731
50.2%
0 211660
 
11.0%
1 160447
 
8.4%
4 128701
 
6.7%
2 98098
 
5.1%
5 89033
 
4.6%
8 86249
 
4.5%
( 64533
 
3.4%
) 64533
 
3.4%
6 20111
 
1.0%
Other values (4) 32448
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5930265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
962731
16.2%
R 424764
 
7.2%
D 411415
 
6.9%
A 403254
 
6.8%
G 328291
 
5.5%
N 299842
 
5.1%
E 247636
 
4.2%
B 229321
 
3.9%
0 211660
 
3.6%
C 194682
 
3.3%
Other values (27) 2216669
37.4%

Base_Volume
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25829
Distinct (%)19.5%
Missing88602
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean1028.9056
Minimum0.1
Maximum23900.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:22.659770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile17
Q1199.4
median522.9
Q31233.7
95-th percentile3571.98
Maximum23900.2
Range23900.1
Interquartile range (IQR)1034.3

Descriptive statistics

Standard deviation1606.635
Coefficient of variation (CV)1.5614988
Kurtosis35.754892
Mean1028.9056
Median Absolute Deviation (MAD)398.2
Skewness4.8651229
Sum1.3644009 × 108
Variance2581275.9
MonotonicityNot monotonic
2023-04-23T21:27:22.729356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 615
 
0.3%
1 370
 
0.2%
0.4 348
 
0.2%
2.4 311
 
0.1%
2 294
 
0.1%
0.3 275
 
0.1%
0.1 253
 
0.1%
0.8 191
 
0.1%
4.8 176
 
0.1%
3.6 164
 
0.1%
Other values (25819) 129610
58.6%
(Missing) 88602
40.1%
ValueCountFrequency (%)
0.1 253
0.1%
0.2 120
 
0.1%
0.3 275
0.1%
0.4 348
0.2%
0.5 33
 
< 0.1%
0.6 42
 
< 0.1%
0.7 147
 
0.1%
0.8 191
0.1%
0.9 50
 
< 0.1%
1 370
0.2%
ValueCountFrequency (%)
23900.2 3
< 0.1%
21573.8 3
< 0.1%
21312.2 3
< 0.1%
20586.6 3
< 0.1%
20447.6 3
< 0.1%
20283 3
< 0.1%
20057.6 3
< 0.1%
19926.2 3
< 0.1%
19723.6 3
< 0.1%
19556.6 3
< 0.1%

Non_Promo_Volume
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21843
Distinct (%)17.2%
Missing94079
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean1052.7707
Minimum0.1
Maximum21516.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:22.795419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile12.1
Q1190.8
median538.5
Q31291.3
95-th percentile3684.855
Maximum21516.6
Range21516.5
Interquartile range (IQR)1100.5

Descriptive statistics

Standard deviation1595.9048
Coefficient of variation (CV)1.5159092
Kurtosis30.075481
Mean1052.7707
Median Absolute Deviation (MAD)425.9
Skewness4.4470029
Sum1.3383874 × 108
Variance2546912.1
MonotonicityNot monotonic
2023-04-23T21:27:22.854380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 641
 
0.3%
1 384
 
0.2%
2 366
 
0.2%
2.4 352
 
0.2%
0.4 334
 
0.2%
0.3 255
 
0.1%
0.1 236
 
0.1%
4.8 214
 
0.1%
0.8 211
 
0.1%
3.6 197
 
0.1%
Other values (21833) 123940
56.0%
(Missing) 94079
42.5%
ValueCountFrequency (%)
0.1 236
0.1%
0.2 105
 
< 0.1%
0.3 255
0.1%
0.4 334
0.2%
0.5 25
 
< 0.1%
0.6 20
 
< 0.1%
0.7 141
 
0.1%
0.8 211
0.1%
0.9 39
 
< 0.1%
1 384
0.2%
ValueCountFrequency (%)
21516.6 3
< 0.1%
20788.6 3
< 0.1%
20712 3
< 0.1%
20492 3
< 0.1%
19648 3
< 0.1%
19038.6 3
< 0.1%
18881.6 3
< 0.1%
18873.8 3
< 0.1%
18820.4 3
< 0.1%
18276.4 3
< 0.1%

Promo_Volume
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7573
Distinct (%)23.9%
Missing189575
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean820.65886
Minimum0.1
Maximum48910.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.010968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.4
Q126.4
median107.35
Q3495.15
95-th percentile3838.8
Maximum48910.7
Range48910.6
Interquartile range (IQR)468.75

Descriptive statistics

Standard deviation2619.3363
Coefficient of variation (CV)3.191748
Kurtosis96.728681
Mean820.65886
Median Absolute Deviation (MAD)96.45
Skewness8.401294
Sum25960722
Variance6860922.5
MonotonicityNot monotonic
2023-04-23T21:27:23.076194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.9 58
 
< 0.1%
6 57
 
< 0.1%
5.4 56
 
< 0.1%
7.7 55
 
< 0.1%
20.8 54
 
< 0.1%
14.9 53
 
< 0.1%
9 52
 
< 0.1%
20.7 51
 
< 0.1%
8.5 51
 
< 0.1%
12 50
 
< 0.1%
Other values (7563) 31097
 
14.1%
(Missing) 189575
85.7%
ValueCountFrequency (%)
0.1 3
 
< 0.1%
0.2 9
 
< 0.1%
0.3 9
 
< 0.1%
0.4 29
< 0.1%
0.5 20
< 0.1%
0.6 20
< 0.1%
0.7 25
< 0.1%
0.8 20
< 0.1%
0.9 39
< 0.1%
1 29
< 0.1%
ValueCountFrequency (%)
48910.7 5
< 0.1%
45292.5 2
 
< 0.1%
41995.2 5
< 0.1%
40420.4 4
< 0.1%
39388.5 2
 
< 0.1%
38344.9 1
 
< 0.1%
38079.6 5
< 0.1%
36270 2
 
< 0.1%
35800.5 2
 
< 0.1%
35356.5 2
 
< 0.1%

Base_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67766
Distinct (%)51.1%
Missing88602
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean3632.964
Minimum0.080321285
Maximum32741.719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.145821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.080321285
5-th percentile102.2
Q11073.1209
median2477.3091
Q35233.4023
95-th percentile10712.4
Maximum32741.719
Range32741.639
Interquartile range (IQR)4160.2813

Descriptive statistics

Standard deviation3569.5411
Coefficient of variation (CV)0.98254239
Kurtosis4.0723146
Mean3632.964
Median Absolute Deviation (MAD)1728.7906
Skewness1.7633723
Sum4.8175645 × 108
Variance12741624
MonotonicityNot monotonic
2023-04-23T21:27:23.204604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 155
 
0.1%
2.205882353 94
 
< 0.1%
0.6 74
 
< 0.1%
2.1 71
 
< 0.1%
3.8 67
 
< 0.1%
5.8 66
 
< 0.1%
1.9 63
 
< 0.1%
2.9 62
 
< 0.1%
0.8 62
 
< 0.1%
4.8 61
 
< 0.1%
Other values (67756) 131832
59.6%
(Missing) 88602
40.1%
ValueCountFrequency (%)
0.08032128514 3
< 0.1%
0.08130081301 1
 
< 0.1%
0.08230452675 1
 
< 0.1%
0.08333333333 1
 
< 0.1%
0.125 1
 
< 0.1%
0.1666666667 7
< 0.1%
0.1875 2
 
< 0.1%
0.1975308642 1
 
< 0.1%
0.2 3
< 0.1%
0.2409638554 1
 
< 0.1%
ValueCountFrequency (%)
32741.71937 1
 
< 0.1%
28871.62388 1
 
< 0.1%
28419.17403 5
< 0.1%
28117.2 1
 
< 0.1%
28020.15191 1
 
< 0.1%
27620.17444 1
 
< 0.1%
27365.5 3
< 0.1%
27287.71558 1
 
< 0.1%
27168.22735 3
< 0.1%
27106.48226 1
 
< 0.1%

Non_Promo_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56646
Distinct (%)44.6%
Missing94079
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean3717.3165
Minimum0.19753086
Maximum29015.818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.268943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.19753086
5-th percentile59.174748
Q11031.6
median2558.0704
Q35403.6
95-th percentile11105.383
Maximum29015.818
Range29015.62
Interquartile range (IQR)4372

Descriptive statistics

Standard deviation3657.3065
Coefficient of variation (CV)0.98385663
Kurtosis3.4047599
Mean3717.3165
Median Absolute Deviation (MAD)1846.0704
Skewness1.653216
Sum4.7258245 × 108
Variance13375891
MonotonicityNot monotonic
2023-04-23T21:27:23.333956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 173
 
0.1%
2.205882353 98
 
< 0.1%
2.9 80
 
< 0.1%
3.8 80
 
< 0.1%
1.9 75
 
< 0.1%
2.1 73
 
< 0.1%
5.8 71
 
< 0.1%
0.6 68
 
< 0.1%
0.5882352941 63
 
< 0.1%
0.8 62
 
< 0.1%
Other values (56636) 126287
57.1%
(Missing) 94079
42.5%
ValueCountFrequency (%)
0.1975308642 1
 
< 0.1%
0.2 2
 
< 0.1%
0.2891566265 2
 
< 0.1%
0.2926829268 2
 
< 0.1%
0.3 6
 
< 0.1%
0.3529411765 5
 
< 0.1%
0.3855421687 41
< 0.1%
0.3902439024 5
 
< 0.1%
0.3950617284 8
 
< 0.1%
0.4 8
 
< 0.1%
ValueCountFrequency (%)
29015.81781 1
< 0.1%
28850.57471 1
< 0.1%
28345.21264 1
< 0.1%
28182.5 1
< 0.1%
28175.6204 1
< 0.1%
27908.8 1
< 0.1%
27744.91523 1
< 0.1%
27563.32103 1
< 0.1%
27273.71063 1
< 0.1%
27145.5748 1
< 0.1%

Promo_Value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17199
Distinct (%)54.4%
Missing189575
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean2507.6785
Minimum0.4
Maximum126688.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.398757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile25.810091
Q1113.50466
median399.37167
Q31735.8449
95-th percentile11886.4
Maximum126688.3
Range126687.9
Interquartile range (IQR)1622.3402

Descriptive statistics

Standard deviation6869.2414
Coefficient of variation (CV)2.7392831
Kurtosis65.729748
Mean2507.6785
Median Absolute Deviation (MAD)351.24759
Skewness6.8000659
Sum79327902
Variance47186478
MonotonicityNot monotonic
2023-04-23T21:27:23.465862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 19
 
< 0.1%
165.3 15
 
< 0.1%
68.1 11
 
< 0.1%
39.5 10
 
< 0.1%
51.3 10
 
< 0.1%
31.26838235 10
 
< 0.1%
21.4 9
 
< 0.1%
24.8 9
 
< 0.1%
74.7 9
 
< 0.1%
57.5 9
 
< 0.1%
Other values (17189) 31523
 
14.3%
(Missing) 189575
85.7%
ValueCountFrequency (%)
0.4 1
< 0.1%
0.8 2
< 0.1%
0.8035714286 2
< 0.1%
0.8333333333 1
< 0.1%
1 2
< 0.1%
1.052631579 1
< 0.1%
1.447963801 2
< 0.1%
1.5 1
< 0.1%
1.666666667 1
< 0.1%
1.718181818 1
< 0.1%
ValueCountFrequency (%)
126688.3 2
 
< 0.1%
111119.6159 1
 
< 0.1%
110280.4 2
 
< 0.1%
101901.4417 5
< 0.1%
101395 2
 
< 0.1%
100944.4806 1
 
< 0.1%
99105.4 2
 
< 0.1%
96990.6 2
 
< 0.1%
92158.7 2
 
< 0.1%
87779.37996 1
 
< 0.1%

Base_Price_Per_KG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68257
Distinct (%)51.5%
Missing88602
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean5.4256412
Minimum0.35714286
Maximum42.424242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.537892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.35714286
5-th percentile1.4452479
Q12.949965
median5.1258199
Q36.4647452
95-th percentile10.246195
Maximum42.424242
Range42.0671
Interquartile range (IQR)3.5147802

Descriptive statistics

Standard deviation3.9050838
Coefficient of variation (CV)0.71974604
Kurtosis10.314719
Mean5.4256412
Median Absolute Deviation (MAD)2.0089078
Skewness2.754814
Sum719478.01
Variance15.24968
MonotonicityNot monotonic
2023-04-23T21:27:23.597658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.352941176 134
 
0.1%
1.5 128
 
0.1%
1.15 119
 
0.1%
5.882352941 110
 
< 0.1%
2.5 104
 
< 0.1%
2.1 98
 
< 0.1%
2 97
 
< 0.1%
1.75 89
 
< 0.1%
1.666666667 74
 
< 0.1%
4.25 70
 
< 0.1%
Other values (68247) 131584
59.5%
(Missing) 88602
40.1%
ValueCountFrequency (%)
0.3571428571 1
 
< 0.1%
0.4166666667 9
< 0.1%
0.4938271605 1
 
< 0.1%
0.5 5
< 0.1%
0.6172839506 1
 
< 0.1%
0.6666666667 8
< 0.1%
0.7228915663 2
 
< 0.1%
0.7272727273 1
 
< 0.1%
0.7317073171 2
 
< 0.1%
0.7429696497 1
 
< 0.1%
ValueCountFrequency (%)
42.42424242 1
< 0.1%
38.22916667 1
< 0.1%
34.42906574 1
< 0.1%
33.19496658 1
< 0.1%
31.43959792 1
< 0.1%
31.36130035 1
< 0.1%
31.14641359 1
< 0.1%
31.02651048 1
< 0.1%
30.85420041 1
< 0.1%
30.75933806 1
< 0.1%

Non_Promo_Price_Per_KG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60148
Distinct (%)47.3%
Missing94079
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean5.3789893
Minimum0.35714286
Maximum33.198507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.659860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.35714286
5-th percentile1.4450495
Q12.9166667
median5.0684781
Q36.4390047
95-th percentile10.164262
Maximum33.198507
Range32.841364
Interquartile range (IQR)3.522338

Descriptive statistics

Standard deviation3.8633337
Coefficient of variation (CV)0.71822669
Kurtosis10.259256
Mean5.3789893
Median Absolute Deviation (MAD)1.9941571
Skewness2.7380305
Sum683830.91
Variance14.925347
MonotonicityNot monotonic
2023-04-23T21:27:23.726463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.708333333 867
 
0.4%
7.647058824 330
 
0.1%
5.294117647 162
 
0.1%
0.987654321 152
 
0.1%
5.925 151
 
0.1%
1.125 148
 
0.1%
1.15 142
 
0.1%
5.647058824 133
 
0.1%
7.352941176 133
 
0.1%
1.625 133
 
0.1%
Other values (60138) 124779
56.4%
(Missing) 94079
42.5%
ValueCountFrequency (%)
0.3571428571 1
 
< 0.1%
0.4166666667 9
< 0.1%
0.4938271605 1
 
< 0.1%
0.5 6
< 0.1%
0.6172839506 1
 
< 0.1%
0.6666666667 8
< 0.1%
0.7228915663 3
 
< 0.1%
0.7317073171 2
 
< 0.1%
0.7447885044 1
 
< 0.1%
0.7475994513 1
 
< 0.1%
ValueCountFrequency (%)
33.19850653 1
< 0.1%
31.44008134 1
< 0.1%
31.35667964 1
< 0.1%
31.14838378 1
< 0.1%
31.02471657 1
< 0.1%
30.85296835 1
< 0.1%
30.70731598 1
< 0.1%
30.67713399 1
< 0.1%
30.67146283 1
< 0.1%
30.59818396 1
< 0.1%

Promo_Price_Per_KG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17368
Distinct (%)54.9%
Missing189575
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean4.7793283
Minimum0.41666667
Maximum30.123457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.793190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.41666667
5-th percentile1.1444135
Q12.5817931
median3.8623035
Q35.7158644
95-th percentile10.665601
Maximum30.123457
Range29.70679
Interquartile range (IQR)3.1340712

Descriptive statistics

Standard deviation3.7277254
Coefficient of variation (CV)0.77996847
Kurtosis9.8329525
Mean4.7793283
Median Absolute Deviation (MAD)1.5376007
Skewness2.8022569
Sum151189.27
Variance13.895936
MonotonicityNot monotonic
2023-04-23T21:27:23.853967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.25 20
 
< 0.1%
2.941176471 19
 
< 0.1%
2.5 16
 
< 0.1%
1.470588235 15
 
< 0.1%
1.666666667 11
 
< 0.1%
2.066666667 11
 
< 0.1%
3.055555556 11
 
< 0.1%
1.25 11
 
< 0.1%
2.25 11
 
< 0.1%
3.666666667 10
 
< 0.1%
Other values (17358) 31499
 
14.2%
(Missing) 189575
85.7%
ValueCountFrequency (%)
0.4166666667 1
 
< 0.1%
0.418760469 1
 
< 0.1%
0.4212792128 1
 
< 0.1%
0.422979798 5
< 0.1%
0.4444444444 2
 
< 0.1%
0.4539219354 1
 
< 0.1%
0.470917653 1
 
< 0.1%
0.4892601432 1
 
< 0.1%
0.5 2
 
< 0.1%
0.5014326648 1
 
< 0.1%
ValueCountFrequency (%)
30.12345679 2
< 0.1%
30 1
< 0.1%
29.96937009 1
< 0.1%
29.9482278 1
< 0.1%
29.90106545 1
< 0.1%
29.84126984 1
< 0.1%
29.81715893 1
< 0.1%
29.79423868 1
< 0.1%
29.66432475 1
< 0.1%
29.60885149 1
< 0.1%

Distribution_wtd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)0.1%
Missing91859
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean70.047623
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:23.920416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q157
median80
Q391
95-th percentile98
Maximum100
Range99
Interquartile range (IQR)34

Descriptive statistics

Standard deviation27.213001
Coefficient of variation (CV)0.38849285
Kurtosis0.20055124
Mean70.047623
Median Absolute Deviation (MAD)14
Skewness-1.1166167
Sum9060660
Variance740.54741
MonotonicityNot monotonic
2023-04-23T21:27:23.983970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 4173
 
1.9%
92 4075
 
1.8%
90 4013
 
1.8%
93 3861
 
1.7%
89 3845
 
1.7%
94 3785
 
1.7%
95 3433
 
1.6%
88 3339
 
1.5%
97 3324
 
1.5%
96 3278
 
1.5%
Other values (90) 92224
41.7%
(Missing) 91859
41.5%
ValueCountFrequency (%)
1 1865
0.8%
2 711
 
0.3%
3 516
 
0.2%
4 620
 
0.3%
5 907
0.4%
6 1251
0.6%
7 495
 
0.2%
8 350
 
0.2%
9 509
 
0.2%
10 630
 
0.3%
ValueCountFrequency (%)
100 2095
0.9%
99 2890
1.3%
98 3082
1.4%
97 3324
1.5%
96 3278
1.5%
95 3433
1.6%
94 3785
1.7%
93 3861
1.7%
92 4075
1.8%
91 4173
1.9%

Distribution_numeric
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)0.1%
Missing91613
Missing (%)41.4%
Infinite0
Infinite (%)0.0%
Mean61.148816
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-04-23T21:27:24.046070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q145
median68
Q384
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)39

Descriptive statistics

Standard deviation27.682803
Coefficient of variation (CV)0.452712
Kurtosis-0.51557583
Mean61.148816
Median Absolute Deviation (MAD)18
Skewness-0.73570699
Sum7924642
Variance766.33757
MonotonicityNot monotonic
2023-04-23T21:27:24.111049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 2974
 
1.3%
88 2858
 
1.3%
2 2850
 
1.3%
85 2679
 
1.2%
87 2621
 
1.2%
84 2591
 
1.2%
83 2499
 
1.1%
86 2435
 
1.1%
75 2368
 
1.1%
76 2361
 
1.1%
Other values (90) 103360
46.7%
(Missing) 91613
41.4%
ValueCountFrequency (%)
1 2299
1.0%
2 2850
1.3%
3 952
 
0.4%
4 1199
0.5%
5 558
 
0.3%
6 510
 
0.2%
7 417
 
0.2%
8 461
 
0.2%
9 555
 
0.3%
10 770
 
0.3%
ValueCountFrequency (%)
100 331
 
0.1%
99 1087
0.5%
98 2080
0.9%
97 1142
0.5%
96 1219
0.6%
95 1199
0.5%
94 1562
0.7%
93 2353
1.1%
92 1806
0.8%
91 1811
0.8%

Interactions

2023-04-23T21:27:18.564461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.309646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.417774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.494694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.448392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.430840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.370103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.399225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.273266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.237858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.334173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.261451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.269009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.325078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.581276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.629728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.444964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.486080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.566684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.522770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.496044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.437604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.457580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.345744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.305422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.401243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.335539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.346692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.420896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.648275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.691460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.563128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.558218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.633217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.593062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.560780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.500395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.512687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.411068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.373814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.460400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.401397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.410283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.704179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.713265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.755733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.639545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.627305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.696551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.658281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.622708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.565897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.575115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.473005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.437774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.528786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.464998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.480781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.800287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.781679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.822999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.709197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.694986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.766137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.727682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.688749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.630642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.635618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.546806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.506252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.601055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.539845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.554759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.871782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.861926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.885627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.775361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.762105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.826673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.789270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.754268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.784133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.691327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.609379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.569516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.660078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.603393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.618426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.944612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.929911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.941880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.833448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.820746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.879443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.843286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.807343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.853128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.747063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.663525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.622094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.712886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.664127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.672220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.003247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.987537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.003956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.895822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.889661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.943912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.906155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.869873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.912654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.801173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.730528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.683856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.773984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.734400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.741969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.061740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.048447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.070066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:04.962370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.956713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.008122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.976576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.934089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.979260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.857839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.794226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.752266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.835292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.802992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.808849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.120138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.113961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.130787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.020419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.015504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.069218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.037009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.992307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.034891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.915159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.851149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.814915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.895712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.869119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.871195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.200083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.170002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.195509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.087786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.081453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.135123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.102484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.054330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.093597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.976450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.914971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.881091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.956630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.940116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.939687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.270281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.236406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.262069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.154115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.145759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.198031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.174379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.116424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.158996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.034031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.975241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.039309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.010599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.001355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.008541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.334898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.303041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.329565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.214148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.215846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.257291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.236500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.180018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.215236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.089235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.036440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.102654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.068321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.064292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.068649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.394343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.369509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.402567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.278280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.281054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.317214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.298894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.242385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.276330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.153940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.105548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.202155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.132941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.134479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.138774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.453173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.434249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:19.466055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:05.350691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:06.431424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:07.384102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:08.367252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:09.303298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:10.341835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:11.210419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:12.173710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:13.268702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:14.192486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:15.202793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:16.217542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:17.509351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T21:27:18.497784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-23T21:27:24.180633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Week_NumPack_Size_NumCategory_ValueCategory_VolumeBase_VolumeNon_Promo_VolumePromo_VolumeBase_ValueNon_Promo_ValuePromo_ValueBase_Price_Per_KGNon_Promo_Price_Per_KGPromo_Price_Per_KGDistribution_wtdDistribution_numericRetailer_NameCategoryBrandSub_BrandPack_TypePack_SizeModelling_GroupF_Group
Week_Num1.0000.0100.034-0.033-0.035-0.038-0.028-0.011-0.014-0.0140.0360.0400.030-0.021-0.0240.0070.0560.0330.0360.0410.0420.0420.045
Pack_Size_Num0.0101.000-0.187-0.1590.3100.3020.2700.0640.0660.074-0.655-0.649-0.711-0.191-0.1790.1510.6170.5720.6620.5201.0000.7291.000
Category_Value0.034-0.1871.0000.9850.2750.260-0.0380.4030.382-0.0330.1820.1840.042-0.193-0.2920.8530.6410.2030.2040.3220.2430.2310.245
Category_Volume-0.033-0.1590.9851.0000.2970.2810.0020.4160.394-0.0030.1520.154-0.000-0.188-0.2930.7970.5080.1710.1560.2560.1880.1740.190
Base_Volume-0.0350.3100.2750.2971.0000.9690.2670.8810.8470.111-0.436-0.437-0.5090.4750.4110.1640.1420.2000.2590.1860.2400.1760.297
Non_Promo_Volume-0.0380.3020.2600.2810.9691.0000.1700.8540.886-0.005-0.420-0.418-0.4990.4760.4130.1860.1470.2010.2580.1890.2390.1780.302
Promo_Volume-0.0280.270-0.0380.0020.2670.1701.0000.1680.0040.949-0.294-0.431-0.3600.3890.3400.1010.0290.0380.0590.0330.0570.0360.075
Base_Value-0.0110.0640.4030.4160.8810.8540.1681.0000.9630.133-0.028-0.026-0.1270.4680.3830.3910.1080.1160.2260.1670.1620.1370.240
Non_Promo_Value-0.0140.0660.3820.3940.8470.8860.0040.9631.000-0.045-0.022-0.020-0.1500.4620.3810.4280.1090.1200.2340.1690.1610.1380.246
Promo_Value-0.0140.074-0.033-0.0030.111-0.0050.9490.133-0.0451.000-0.023-0.123-0.0750.3910.3440.1200.0380.0490.0710.0340.0670.0400.086
Base_Price_Per_KG0.036-0.6550.1820.152-0.436-0.420-0.294-0.028-0.022-0.0231.0000.9990.930-0.110-0.1130.0870.5580.3970.6730.4670.6480.4380.690
Non_Promo_Price_Per_KG0.040-0.6490.1840.154-0.437-0.418-0.431-0.026-0.020-0.1230.9991.0000.941-0.117-0.1170.1100.6670.3700.7120.4520.6820.5220.748
Promo_Price_Per_KG0.030-0.7110.042-0.000-0.509-0.499-0.360-0.127-0.150-0.0750.9300.9411.000-0.064-0.0470.1300.5740.3280.5640.3810.5660.4570.599
Distribution_wtd-0.021-0.191-0.193-0.1880.4750.4760.3890.4680.4620.391-0.110-0.117-0.0641.0000.9780.2410.1260.1690.2910.2270.2360.1780.316
Distribution_numeric-0.024-0.179-0.292-0.2930.4110.4130.3400.3830.3810.344-0.113-0.117-0.0470.9781.0000.3200.1430.1800.2890.2280.2420.1820.315
Retailer_Name0.0070.1510.8530.7970.1640.1860.1010.3910.4280.1200.0870.1100.1300.2410.3201.0000.0850.3150.1790.0570.1830.1150.199
Category0.0560.6170.6410.5080.1420.1470.0290.1080.1090.0380.5580.6670.5740.1260.1430.0851.0000.4990.8680.6971.0001.0001.000
Brand0.0330.5720.2030.1710.2000.2010.0380.1160.1200.0490.3970.3700.3280.1690.1800.3150.4991.0000.8810.5670.5770.4770.944
Sub_Brand0.0360.6620.2040.1560.2590.2580.0590.2260.2340.0710.6730.7120.5640.2910.2890.1790.8680.8811.0000.8910.7570.5870.784
Pack_Type0.0410.5200.3220.2560.1860.1890.0330.1670.1690.0340.4670.4520.3810.2270.2280.0570.6970.5670.8911.0000.8730.7290.998
Pack_Size0.0421.0000.2430.1880.2400.2390.0570.1620.1610.0670.6480.6820.5660.2360.2420.1831.0000.5770.7570.8731.0000.5850.943
Modelling_Group0.0420.7290.2310.1740.1760.1780.0360.1370.1380.0400.4380.5220.4570.1780.1820.1151.0000.4770.5870.7290.5851.0000.756
F_Group0.0451.0000.2450.1900.2970.3020.0750.2400.2460.0860.6900.7480.5990.3160.3150.1991.0000.9440.7840.9980.9430.7561.000

Missing values

2023-04-23T21:27:19.719620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-23T21:27:20.341546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-23T21:27:21.138472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PPGWeekWeek_NumRetailer_NameCategoryBrandSub_BrandPack_TypePack_SizePack_Size_NumCategory_ValueCategory_VolumeModelling_GroupF_GroupBase_VolumeNon_Promo_VolumePromo_VolumeBase_ValueNon_Promo_ValuePromo_ValueBase_Price_Per_KGNon_Promo_Price_Per_KGPromo_Price_Per_KGDistribution_wtdDistribution_numeric
0BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 219Retailer ADRYBRAND ASUBBRAND A.1ZAK1500_G1500323026.490161.0BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG94.3NaN148.5267.583758NaN341.92.837580NaN2.3023577.03.0
1BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 219Retailer BDRYBRAND ASUBBRAND A.1ZAK1500_G150047785.216947.6BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 219Retailer CDRYBRAND ASUBBRAND A.1ZAK1500_G1500232030.174702.1BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG184.2264.0NaN501.400000719.9NaN2.7220412.726894NaN24.016.0
3BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2210Retailer ADRYBRAND ASUBBRAND A.1ZAK1500_G1500315345.888364.6BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG55.587.0NaN158.900000248.8NaN2.8630632.859770NaN6.03.0
4BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2210Retailer BDRYBRAND ASUBBRAND A.1ZAK1500_G1500103000.833226.4BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2210Retailer CDRYBRAND ASUBBRAND A.1ZAK1500_G1500217268.069354.0BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG157.9253.5NaN436.261855701.3NaN2.7629002.766469NaN23.016.0
6BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2311Retailer ADRYBRAND ASUBBRAND A.1ZAK1500_G1500307502.085380.4BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG48.293.0NaN137.943095266.0NaN2.8618902.860215NaN6.03.0
7BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2311Retailer BDRYBRAND ASUBBRAND A.1ZAK1500_G150078540.432781.1BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2311Retailer CDRYBRAND ASUBBRAND A.1ZAK1500_G1500218625.269515.0BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG321.3262.5NaN882.200000722.3NaN2.7457212.751619NaN21.015.0
9BRAND A SUBBRAND A.1 ADULT ZAK GROENTE_KIP_RIJST 1500_Gwk 18 2412Retailer ADRYBRAND ASUBBRAND A.1ZAK1500_G1500338839.897684.8BRAND B DRY NUGGETS REGULAR 3X3KGBRAND A DRY 1,5KG87.385.5NaN249.600000244.5NaN2.8591072.859649NaN7.03.0
PPGWeekWeek_NumRetailer_NameCategoryBrandSub_BrandPack_TypePack_SizePack_Size_NumCategory_ValueCategory_VolumeModelling_GroupF_GroupBase_VolumeNon_Promo_VolumePromo_VolumeBase_ValueNon_Promo_ValuePromo_ValueBase_Price_Per_KGNon_Promo_Price_Per_KGPromo_Price_Per_KGDistribution_wtdDistribution_numeric
221199BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 37182Retailer CSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221200BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 38183Retailer ASNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221201BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 38183Retailer BSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221202BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 38183Retailer CSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221203BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 39184Retailer ASNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221204BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 39184Retailer BSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221205BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 39184Retailer CSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221206BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 40185Retailer ASNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221207BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 40185Retailer BSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
221208BRAND O SUBBRAND O.6 ADULT BAK ZEEVRUCHTEN 60_Gwk 21 40185Retailer CSNACKSBRAND OSUBBRAND O.6BAK60_G60NaNNaNBRAND D SNACKS PARTY MIX 60GBRAND O SNACKS 60GNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN